


'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase__          = logging.get_logger(__name__)

UpperCamelCase__          = {
    '''google/switch-base-8''': '''https://huggingface.co/google/switch-base-8/blob/main/config.json''',
}




class      lowerCamelCase_  (      __a ):
      lowerCAmelCase__							=	'switch_transformers'
      lowerCAmelCase__							=	['past_key_values']
      lowerCAmelCase__							=	{'hidden_size': 'd_model', 'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}






      def __init__(   self							:       Any		,		_A							:       List[Any]=32_128		,		_A							:       Dict=768		,		_A							:       List[str]=64		,		_A							:       List[Any]=2_048		,		_A							:       List[Any]=64		,		_A							:       int=12		,		_A							:       int=3		,		_A							:       List[Any]=12		,		_A							:       Any=3		,		_A							:       Optional[Any]=12		,		_A							:       Dict=8		,		_A							:       int=False		,		_A							:       int=0.0_1		,		_A							:       int="float32"		,		_A							:       Union[str, Any]=False		,		_A							:       Tuple=32		,		_A							:       Any=128		,		_A							:       str=0.1		,		_A							:       List[Any]=1e-6		,		_A							:       str=0.0_0_1		,		_A							:       Optional[int]=0.0_0_1		,		_A							:       Optional[int]=1.0		,		_A							:       List[Any]="relu"		,		_A							:       List[str]=True		,		_A							:       Optional[int]=False		,		_A							:       int=True		,		_A							:       List[Any]=0		,		_A							:       List[str]=1		,		**_A							:       List[str]		,		):


          '''simple docstring'''


          UpperCAmelCase__     :						Dict						=							vocab_size
          UpperCAmelCase__     :						Union[str, Any]						=							d_model
          UpperCAmelCase__     :						List[Any]						=							d_kv
          UpperCAmelCase__     :						List[Any]						=							d_ff

          UpperCAmelCase__     :						Any						=							num_sparse_encoder_layers

          UpperCAmelCase__     :						List[str]						=							num_layers
          UpperCAmelCase__     :						Optional[Any]						=							(
              num_decoder_layers if num_decoder_layers is not None else self.num_layers
          )  # default = symmetry
          UpperCAmelCase__     :						List[str]						=							num_sparse_decoder_layers

          # This tells us, each how many encoder layer we'll have to set a sparse layer.
          if self.num_sparse_encoder_layers > 0:
              UpperCAmelCase__     :						int						=							self.num_layers // self.num_sparse_encoder_layers
          else:
              UpperCAmelCase__     :						Optional[int]						=							self.num_layers  # HACK: this will create 0 sparse layers

          # This tells us, each how many encoder layer we'll have to set a sparse layer.
          if self.num_sparse_decoder_layers > 0:
              UpperCAmelCase__     :						Optional[int]						=							self.num_decoder_layers // self.num_sparse_decoder_layers
          else:
              UpperCAmelCase__     :						List[str]						=							self.num_decoder_layers  # HACK: this will create 0 sparse layers

          UpperCAmelCase__     :						Tuple						=							num_heads
          UpperCAmelCase__     :						Tuple						=							num_experts
          UpperCAmelCase__     :						Union[str, Any]						=							expert_capacity
          UpperCAmelCase__     :						str						=							router_bias
          UpperCAmelCase__     :						Any						=							router_jitter_noise
          if router_dtype not in ["float32", "float16", "bfloat16"]:
              raise ValueError(f"""`router_dtype` must be one of 'float32', 'float16' or 'bfloat16', got {router_dtype}"""					)
          UpperCAmelCase__     :						Any						=							router_dtype

          UpperCAmelCase__     :						str						=							router_ignore_padding_tokens
          UpperCAmelCase__     :						str						=							relative_attention_num_buckets
          UpperCAmelCase__     :						str						=							relative_attention_max_distance

          UpperCAmelCase__     :						Optional[Any]						=							dropout_rate
          UpperCAmelCase__     :						Tuple						=							layer_norm_epsilon
          UpperCAmelCase__     :						Optional[int]						=							initializer_factor
          UpperCAmelCase__     :						int						=							feed_forward_proj
          UpperCAmelCase__     :						Optional[int]						=							use_cache
          UpperCAmelCase__     :						Optional[int]						=							add_router_probs

          UpperCAmelCase__     :						Any						=							router_z_loss_coef
          UpperCAmelCase__     :						Tuple						=							router_aux_loss_coef

          UpperCAmelCase__     :						List[str]						=							self.feed_forward_proj.split('''-'''					)
          UpperCAmelCase__     :						Union[str, Any]						=							act_info[-1]
          UpperCAmelCase__     :						Any						=							act_info[0] == '''gated'''

          if len(_A					) > 1 and act_info[0] != "gated" or len(_A					) > 2:
              raise ValueError(
                  f"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
                  '''Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '''
                  '''\'gated-gelu\' or \'relu\''''					)

          # for backwards compatibility
          if feed_forward_proj == "gated-gelu":
              UpperCAmelCase__     :						List[Any]						=							'''gelu_new'''

          super().__init__(
              pad_token_id=_A		,		eos_token_id=_A		,		is_encoder_decoder=_A		,		**_A		,		)








'''simple docstring'''





def __UpperCAmelCase   (					A					:			list		)    ->   list:
			if len(A		) <= 1:
						return lst

			UpperCAmelCase_      :       List[str]          =       1

			while i < len(A		):
						if lst[i - 1] <= lst[i]:
									i += 1
						else:
									UpperCAmelCase_    ,      UpperCAmelCase_      :       Dict          =       lst[i], lst[i - 1]
									i -= 1
									if i == 0:
												UpperCAmelCase_      :       Dict          =       1

			return lst


if __name__ == "__main__":
						_UpperCamelCase						:						int  =					input('Enter numbers separated by a comma:\n').strip()
						_UpperCamelCase						:						Union[str, Any]  =					[int(item) for item in user_input.split(',')]
						print(gnome_sort(unsorted))


